Skip to main content
Glama
JingYangYuan

zotero-local-mcp

by JingYangYuan

zotero_get_search_database_status

Check the semantic search database's readiness and stats, including item count and last update, to determine if a re-index is needed before searching.

Instructions

Report the semantic search database's readiness and stats: item count, last update time, embedding provider / model, and whether the [semantic] optional dependency is installed. Use this to decide whether zotero_semantic_search will return useful results, or whether the user should run zotero_update_search_database first. Takes no parameters; no side effects. Returns a human-readable status block. If the [semantic] extras are not installed, returns an install hint instead of stats. Example: zotero_get_search_database_status() → count, last sync, provider summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses no side effects, describes the conditional return (install hint if extras missing), and states it returns a human-readable status block. This is transparent about behavior beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, front-loading the purpose, then usage, behavior, and an example. Every sentence adds value, and the example clarifies the return. It is appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists (per context signals), the description doesn't need to detail return structure, but it still provides a human-readable status block and an example. It also covers the conditional install-hint behavior and ties to sibling tools. Complete for an agent to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so the schema already covers everything (coverage 100%). The description adds no parameter information since none exist, but it does add context about return values. Baseline for 0 params is 4, which is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reports readiness and stats for the semantic search database, listing specific items (item count, last update, provider/model, optional dependency). It also ties to sibling tools, distinguishing from zotero_semantic_search and zotero_update_search_database. This is a specific verb+resource with clear scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: to decide whether zotero_semantic_search will return useful results or whether to run zotero_update_search_database first. It also notes it takes no parameters and has no side effects, giving clear context and routing to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.